Quantitative Prediction of Repaglinide-Rifampicin Complex Drug Interactions Using Dynamic and Static Mechanistic Models: Delineating Differential CYP3A4 Induction and OATP1B1 Inhibition Potential of Rifampicin

2013 ◽  
Vol 41 (5) ◽  
pp. 966-974 ◽  
Author(s):  
Manthena V. S. Varma ◽  
Jian Lin ◽  
Yi-An Bi ◽  
Charles J. Rotter ◽  
Odette A. Fahmi ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Quantitative Prediction ◽  
Mechanistic Models ◽  
Cyp3a4 Induction ◽  
Complex Drug

scholarly journals Dealing with the complex drug-drug interactions: Towards mechanistic models

2015 ◽  
Vol 36 (2) ◽  
pp. 71-92 ◽  
Author(s):  
Manthena V. Varma ◽  
K. Sandy Pang ◽  
Nina Isoherranen ◽  
Ping Zhao
Keyword(s):  
Drug Interactions ◽  
Mechanistic Models ◽  
Complex Drug

scholarly journals Comprehensive PBPK Model of Rifampicin for Quantitative Prediction of Complex Drug-Drug Interactions: CYP3A/2C9 Induction and OATP Inhibition Effects

2018 ◽  
Vol 7 (3) ◽  
pp. 186-196 ◽  
Author(s):  
Ryuta Asaumi ◽  
Kota Toshimoto ◽  
Yoshifusa Tobe ◽  
Kenta Hashizume ◽  
Ken-ichi Nunoya ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Pbpk Model ◽  
Quantitative Prediction ◽  
Complex Drug

Theoretical Considerations on Quantitative Prediction of Drug-Drug Interactions

2010 ◽  
Vol 25 (1) ◽  
pp. 48-61 ◽  
Author(s):  
Akihiro Hisaka ◽  
Yoshiyuki Ohno ◽  
Takehito Yamamoto ◽  
Hiroshi Suzuki
Keyword(s):  
Drug Interactions ◽  
Quantitative Prediction ◽  
Theoretical Considerations

scholarly journals Opioids, Polypharmacy, and Drug Interactions: A Technological Paradigm Shift Is Needed to Ameliorate the Ongoing Opioid Epidemic

Pharmacy ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 154
Author(s):  
Adriana Matos ◽  
David L. Bankes ◽  
Kevin T. Bain ◽  
Tyler Ballinghoff ◽  
Jacques Turgeon
Keyword(s):  
Drug Interactions ◽  
Paradigm Shift ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Health Records ◽  
Technological Paradigm ◽  
Life Threatening ◽  
Drug Regimens ◽  
Manual Input ◽  
Complex Drug

Polypharmacy is a common phenomenon among adults using opioids, which may influence the frequency, severity, and complexity of drug–drug interactions (DDIs) experienced. Clinicians must be able to easily identify and resolve DDIs since opioid-related DDIs are common and can be life-threatening. Given that clinicians often rely on technological aids—such as clinical decision support systems (CDSS) and drug interaction software—to identify and resolve DDIs in patients with complex drug regimens, this narrative review provides an appraisal of the performance of existing technologies. Opioid-specific CDSS have several system- and content-related limitations that need to be overcome. Specifically, we found that these CDSS often analyze DDIs in a pairwise manner, do not account for relevant pharmacogenomic results, and do not integrate well with electronic health records. In the context of polypharmacy, existing systems may encourage inadvertent serious alert dismissal due to the generation of multiple incoherent alerts. Future technological systems should minimize alert fatigue, limit manual input, allow for simultaneous multidrug interaction assessments, incorporate pharmacogenomic data, conduct iterative risk simulations, and integrate seamlessly with normal workflow.

scholarly journals Fluoxetine- and Norfluoxetine-Mediated Complex Drug–Drug Interactions: In Vitro to In Vivo Correlation of Effects on CYP2D6, CYP2C19, and CYP3A4

2014 ◽  
Vol 95 (6) ◽  
pp. 653-662 ◽  
Author(s):  
J E Sager ◽  
J D Lutz ◽  
R S Foti ◽  
C Davis ◽  
K L Kunze ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Complex Drug
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